• Journal of IKEEE
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• v.23 no.2
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• pp.743-746
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• 2019

In this paper we proposed a new ocean radiation automatic monitoring system. The proposed system has the following characteristics: First, using NaI + PVT mixed detectors, the response speed is fast and precision analysis is possible. Second, the application of temperature compensation algorithm to scintillator-type sensors does not require additional cooling devices and enables stable operation in the changing ocean environment. Third, since cooling system is not needed, electricity consumption is low, and electricity can be supplied reliably by utilizing solar energy, which can be installed at the observation deck of ocean environment. Fourth, using GPS and wireless communications, accurate location information and real-time data transmission function for measurement areas enables immediate warning response in the event of nuclear accidents such as those involving neighboring countries. The results tested by the authorized testing agency to assess the performance of the proposed system were measured in the range of $5{\mu}Sv/h$ to 15mSv/h, which is the highest level in the world, and the accuracy was determined to be ${\pm}8.1%$, making normal operation below the international standard ${\pm}15%$. The internal environmental grade (waterproof) was achieved, and the rate of variation was measured within 5% at operating temperature of $-20^{\circ}C$ to $50^{\circ}C$ and stability was verified. Since the measured value change rate was measured within 10% after the vibration test, it was confirmed that there will be no change in the measured value due to vibration in the ocean environment caused by waves.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.29-38
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• 2021

The Internet including mobile networks is developing to overcoming the limitation of physical distance and providing or acquiring information from remote locations. However, the systems that use video as primary information require higher bandwidth for recognizing the situation in remote places more accurately through high-quality video as well as lower latency for faster interaction between devices and users. The emergence of the 5th generation mobile network provides features such as high bandwidth and precise location recognition that were not experienced in previous-generation technologies. In addition, the Mobile Edge Computing that minimizes network latency in the mobile network requires a change in the traditional system architecture that was composed of the existing smart device and high availability server system. However, even with 5G and MEC, since there is a limit to overcome the mobile network state fluctuations only by enhancing the network infrastructure, this study proposes a high-definition video streaming system in ultra-low latency based on the SRT protocol that provides Forward Error Correction and Fast Retransmission. The proposed system shows how to deploy software components that are developed in consideration of the nature of 5G and MEC to achieve sub-1 second latency for 4K real-time video streaming. In the last of this paper, we analyze the most significant factor in the entire video transmission process to achieve the lowest possible latency.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.590-596
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• 2020

In the island areas where system connection with the commercial power grid is difficult, it is quite important to find a method to efficiently manage energy produced with independent microgrids. In this paper, a prosumer management system for P2P power transaction was realized through the testing the power meter and the response rate of the collected data for the power produced in the small-scale microgrids in which hybrid models of solar power and wind power were implemented. The power network of the microgrid prosumer was composed of mesh structure and the P2P power transaction was tested through the power meter and DC power transmitter in the off-grid sites which were independently constructed in three places. The measurement values of the power meter showed significant results of voltage (average): 380V + 0.9V, current (average): + 0.01A, power: 1000W (-1W) with an error range within ±1%. Stabilization of the server was also confirmed with the response rate of 0.32 sec. for the main screen, 2.61 sec. for the cumulative power generation, and 0.11 sec for the power transaction through the transmission of 50 data in real time. Therefore, the proposed system was validated as a P2P power transaction system that can be used as an independent network without transmitted by Korea Electric Power Corporation (KEPCO).

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.259-267
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• 2021

Recently, the market for personal health care and medical devices based on Bluetooth Low Energy(BLE) has grown rapidly. BLE is being used in various medical data communication devices based on low power consumption and universal compatibility. However, since data errors occurring in the transmission of medical data can lead to medical accidents, it is necessary to analyze the causes of errors and study methods to reduce data error. In this paper, the minimum communication speed to be used in medical devices was set to at least 800 byte/sec based on the wireless electrocardiography regulations of the Ministry of Food and Drug Safety. And the data loss rate was tested when data was transmitted at a speed higher than 800 byte/sec. The factors that cause communication data error were classified, and the relationship between each factor and the data error rate was analyzed through experiments. When there were two or more activated peripherals connected to the central, data error occurred due to channel hopping and bottleneck, and the data error rate increased in proportion to the communication distance and the number of activated peripherals. Through this experiment, when the BLE is used in a medical device that intermittently transmits biosignal data, the risk of a medical accident is predicted to be low if the number of peripherals is 3 or less. But, it was determined that BLE would not be suitable for the development of a biosignal measuring device that must be continuously transmitted in real time, such as an electrocardiogram.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.172-178
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• 2022

In this paper, water meter AMR(automatic meter reading), one of the core technologies of smart water grid, using LoRa IoT network is studied. The main content of the research is to develop the network system and show the test results that one PC server receives the readings of water meters from multiple households through LoRa communication and stores them in the database, and at the same time sends the data to the web server database through internet. The system also allows users to monitor the meter readings using their smartphones. The hardware and firmware of the main board of the digital water meter are developed. For a PC server program, MDMS(meter data management system) is developed using Visual C#. The app program running on the user's smartphone is also developed using Android Studio. By connecting each developed parts, the total network system is mounted on a flow test bench in the laboratory and tested. For the fields test, 5 places around the university are selected and the transmission distances are tested. The test result show that the developed system can be applied into the real field. The developed system can be expanded to various social safety nets such as monitoring the living alone or elderly with dementia.

• Journal of Appropriate Technology
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• v.7 no.2
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• pp.196-205
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• 2021

As industrial safety increases, various industrial accident prevention technologies using smart factory technology are being studied. However, small and medium enterprises (SMEs), which account for the majority of industrial accidents, are having difficulties in preventing industrial accidents by applying these smart factory technologies due to practical problems. In this study, customized monitoring and warning systems for each type of industrial accident were developed and applied to the actual field. Through this, we demonstrated industrial accident prevention technology through appropriate smart factory technology used by SMEs. A customized monitoring system using vision, current, temperature, and gas sensors was established for the four major disaster types: worker body access, short circuit and overcurrent, fire and burns due to high temperature, and emission of hazardous gas. In addition, a notification method suitable for each work environment was applied so that the monitored risk factors could be recognized quickly, and real-time data transmission and display enabled workers and managers to understand the disaster risk effectively. Through the application and demonstration of these appropriate smart factory technologies, the spread of these industrial safety technologies is to be discussed.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.77-84
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• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.85-93
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• 2024

The monitoring of environmental information (e.g. noise, dust, vibration, temperature, humidity) is crucial to the safe and sustainable operation of a construction site. However, commercial sensors exhibit certain drawbacks when applied on-site. First, the installation cost is prohibitively high. Second, these sensors have been engineered without considering the rugged and harsh conditions of a construction site, resulting in error-prone sensing. Third, construction sites are compelled to allocate additional resources in terms of manpower, expenses, and physical spaces to accommodate individual sensors. This research developed an integrated sensing module to measure the environmental information in construction site. The sensing module slashes the installation cost to 3.3%, is robust enough to harsh and outdoor sites, and consolidates multiple sensors into a single unit. The sensing module also supports GPS, LTE, and real-time sensing. The evaluation showed remarkable results including 97.5% accuracy and 99.9% precision in noise measurement, an 89.7% accuracy in dust measurement, and a 93.5% reliability in data transmission. This research empowers the collection of substantial volumes and high-quality environmental data from construction sites, providing invaluable support to decision-making process. These encompass objective regulatory compliance checking, simulations of environmental data dispersion, and the development of environmental mitigation strategies.